DS Pipeline

Data Augmentation for ML you own this product

This project is part of the liveProject series DS Pipeline with Python
prerequisites
basic Python (Jupyter Notebook, SciPy, NLTK) • basic data augmentation • basic statistical modeling • basic object-oriented programming (OOP)
skills learned
data augmentation for ML • deal with missing data with statistical modeling • build data augmentation tools with OOP
Ruihao Qiu
1 week · 6-8 hours per week · INTERMEDIATE
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liveProject This project is part of the liveProject series DS Pipeline with Python liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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As a machine learning engineer in an online recruiting tech company or HR department of a large organization, your task is to address a lack of data, a common problem in data science projects. To solve this, you’ll create multiple tools to augment processed data, increasing its volume and learning essentials about probability distributions, random sampling, and OOP. Completing this project will enhance your data analysis and visualization skills, taking you further down the path to a career in data science.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Ruihao Qiu

Ruihao Qiu is the senior data scientist at a German tech company and has more than five years experience in data science and machine learning. As part of the process of earning his PhD in statistical physics, he developed statistical models to simulate and search for new nanomaterials. In his early days as a data science consultant, he helped his clients from DAX30 multinational companies solve real-world data challenges. As a senior data scientist, he designed and built data pipeline and ML recommender systems for online recruitment applications. He enjoys taking on different career roles and sharing ideas about his data science work in tech blogs and in public presentations.

prerequisites

This liveProject is for Python beginners who want to learn how to improve their data analysis skills with data augmentation. To begin these liveProjects you’ll need to be familiar with the following:

TOOLS
  • Basic Python
  • Basic Jupyter Notebook/JupyterLab
  • Basic SciPy
  • Intermediate pandas
  • Basic plotting with Matplotlib
  • Basics of NLTK (Natural Language Toolkit)
TECHNIQUES
  • Basic statistics
  • Basics of plot types
  • Basics of object-oriented programming (OOP)

you will learn

In this liveProject, you’ll learn to create data augmentation tools to deal with missing data.

  • Use SciPy stats module for probability distribution fitting
  • Enhance your pandas and Matplotlib skills
  • Use tools for augmenting text and time series data
  • Find probability distributions and random sampling
  • Deal with missing data

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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